Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations34417
Missing cells87581
Missing cells (%)11.6%
Duplicate rows564
Duplicate rows (%)1.6%
Total size in memory27.6 MiB
Average record size in memory841.0 B

Variable types

Numeric10
Categorical4
Boolean6
Text2

Alerts

IsMemberFlag has constant value "False" Constant
Dataset has 564 (1.6%) duplicate rowsDuplicates
AcademicDegreeLevel is highly overall correlated with IsAlumnusFlagHigh correlation
CumulativeDonationAmount is highly overall correlated with DonorIndicatorFlag.High correlation
DonorIndicatorFlag. is highly overall correlated with CumulativeDonationAmountHigh correlation
IsAlumnusFlag is highly overall correlated with AcademicDegreeLevelHigh correlation
MaritalStatus is highly imbalanced (57.9%) Imbalance
IsParentFlag is highly imbalanced (60.4%) Imbalance
PreferredAddressType is highly imbalanced (81.2%) Imbalance
MaritalStatus has 24507 (71.2%) missing values Missing
GenderIdentity has 492 (1.4%) missing values Missing
WealthRating has 31716 (92.2%) missing values Missing
AcademicDegreeLevel has 26835 (78.0%) missing values Missing
PreferredAddressType has 4031 (11.7%) missing values Missing
CumulativeDonationAmount is highly skewed (γ1 = 94.32672791) Skewed
ConsecutiveDonorYears has 18180 (52.8%) zeros Zeros
LastFiscalYearDonation has 32231 (93.6%) zeros Zeros
Donation2FiscalYearsAgo has 32156 (93.4%) zeros Zeros
Donation3FiscalYearsAgo has 32060 (93.2%) zeros Zeros
Donation4FiscalYearsAgo has 32407 (94.2%) zeros Zeros
Donation5FiscalYearsAgo has 32551 (94.6%) zeros Zeros
CurrentFiscalYearDonation has 32663 (94.9%) zeros Zeros
CumulativeDonationAmount has 13043 (37.9%) zeros Zeros

Reproduction

Analysis started2025-04-04 19:09:06.507894
Analysis finished2025-04-04 19:11:09.936015
Duration2 minutes and 3.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

DonorAge
Real number (ℝ)

Distinct102
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.367638
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:10.078058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26
Q142
median42
Q342
95-th percentile69
Maximum110
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.408334
Coefficient of variation (CV)0.26306099
Kurtosis4.296413
Mean43.367638
Median Absolute Deviation (MAD)0
Skewness1.4383434
Sum1492584
Variance130.15008
MonotonicityNot monotonic
2025-04-04T19:11:10.271659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 21416
62.2%
32 381
 
1.1%
30 366
 
1.1%
31 351
 
1.0%
27 336
 
1.0%
33 331
 
1.0%
35 325
 
0.9%
29 322
 
0.9%
34 317
 
0.9%
37 308
 
0.9%
Other values (92) 9964
29.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 12
< 0.1%
4 9
< 0.1%
5 14
< 0.1%
6 6
< 0.1%
7 13
< 0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
10 6
< 0.1%
11 10
< 0.1%
ValueCountFrequency (%)
110 2
 
< 0.1%
103 1
 
< 0.1%
101 1
 
< 0.1%
100 2
 
< 0.1%
99 2
 
< 0.1%
98 5
 
< 0.1%
97 5
 
< 0.1%
96 11
< 0.1%
95 8
 
< 0.1%
94 26
0.1%

MaritalStatus
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)0.1%
Missing24507
Missing (%)71.2%
Memory size3.4 MiB
Married
6524 
Single
3133 
Widowed
 
157
Divorced
 
84
Separated
 
9

Length

Max length13
Median length7
Mean length6.6959637
Min length6

Characters and Unicode

Total characters66357
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 6524
 
19.0%
Single 3133
 
9.1%
Widowed 157
 
0.5%
Divorced 84
 
0.2%
Separated 9
 
< 0.1%
Never Married 3
 
< 0.1%
(Missing) 24507
71.2%

Length

2025-04-04T19:11:10.449224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T19:11:10.584441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 6527
65.8%
single 3133
31.6%
widowed 157
 
1.6%
divorced 84
 
0.8%
separated 9
 
0.1%
never 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 13150
19.8%
e 9925
15.0%
i 9901
14.9%
d 6934
10.4%
a 6545
9.9%
M 6527
9.8%
S 3142
 
4.7%
n 3133
 
4.7%
g 3133
 
4.7%
l 3133
 
4.7%
Other values (10) 834
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 13150
19.8%
e 9925
15.0%
i 9901
14.9%
d 6934
10.4%
a 6545
9.9%
M 6527
9.8%
S 3142
 
4.7%
n 3133
 
4.7%
g 3133
 
4.7%
l 3133
 
4.7%
Other values (10) 834
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 13150
19.8%
e 9925
15.0%
i 9901
14.9%
d 6934
10.4%
a 6545
9.9%
M 6527
9.8%
S 3142
 
4.7%
n 3133
 
4.7%
g 3133
 
4.7%
l 3133
 
4.7%
Other values (10) 834
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 13150
19.8%
e 9925
15.0%
i 9901
14.9%
d 6934
10.4%
a 6545
9.9%
M 6527
9.8%
S 3142
 
4.7%
n 3133
 
4.7%
g 3133
 
4.7%
l 3133
 
4.7%
Other values (10) 834
 
1.3%

GenderIdentity
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing492
Missing (%)1.4%
Memory size3.3 MiB
Female
16633 
Male
16191 
Uknown
 
1088
Unknown
 
12
U
 
1

Length

Max length7
Median length6
Mean length5.045689
Min length1

Characters and Unicode

Total characters171175
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 16633
48.3%
Male 16191
47.0%
Uknown 1088
 
3.2%
Unknown 12
 
< 0.1%
U 1
 
< 0.1%
(Missing) 492
 
1.4%

Length

2025-04-04T19:11:10.766628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T19:11:10.877589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 16633
49.0%
male 16191
47.7%
uknown 1088
 
3.2%
unknown 12
 
< 0.1%
u 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 49457
28.9%
l 32824
19.2%
a 32824
19.2%
m 16633
 
9.7%
F 16633
 
9.7%
M 16191
 
9.5%
n 2212
 
1.3%
U 1101
 
0.6%
k 1100
 
0.6%
o 1100
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49457
28.9%
l 32824
19.2%
a 32824
19.2%
m 16633
 
9.7%
F 16633
 
9.7%
M 16191
 
9.5%
n 2212
 
1.3%
U 1101
 
0.6%
k 1100
 
0.6%
o 1100
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49457
28.9%
l 32824
19.2%
a 32824
19.2%
m 16633
 
9.7%
F 16633
 
9.7%
M 16191
 
9.5%
n 2212
 
1.3%
U 1101
 
0.6%
k 1100
 
0.6%
o 1100
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49457
28.9%
l 32824
19.2%
a 32824
19.2%
m 16633
 
9.7%
F 16633
 
9.7%
M 16191
 
9.5%
n 2212
 
1.3%
U 1101
 
0.6%
k 1100
 
0.6%
o 1100
 
0.6%

IsMemberFlag
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
34417 
ValueCountFrequency (%)
False 34417
100.0%
2025-04-04T19:11:10.959826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

IsAlumnusFlag
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
26021 
True
8396 
ValueCountFrequency (%)
False 26021
75.6%
True 8396
 
24.4%
2025-04-04T19:11:11.019478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

IsParentFlag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
31726 
True
 
2691
ValueCountFrequency (%)
False 31726
92.2%
True 2691
 
7.8%
2025-04-04T19:11:11.092308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
26468 
True
7949 
ValueCountFrequency (%)
False 26468
76.9%
True 7949
 
23.1%
2025-04-04T19:11:11.162393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

WealthRating
Real number (ℝ)

Missing 

Distinct8
Distinct (%)0.3%
Missing31716
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean2.9307664
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:11.254815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.51536
Coefficient of variation (CV)0.51705248
Kurtosis-0.19021605
Mean2.9307664
Median Absolute Deviation (MAD)1
Skewness0.55064514
Sum7916
Variance2.296316
MonotonicityNot monotonic
2025-04-04T19:11:11.383762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 643
 
1.9%
1 579
 
1.7%
2 563
 
1.6%
4 509
 
1.5%
5 263
 
0.8%
6 81
 
0.2%
7 59
 
0.2%
8 4
 
< 0.1%
(Missing) 31716
92.2%
ValueCountFrequency (%)
1 579
1.7%
2 563
1.6%
3 643
1.9%
4 509
1.5%
5 263
0.8%
6 81
 
0.2%
7 59
 
0.2%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 59
 
0.2%
6 81
 
0.2%
5 263
0.8%
4 509
1.5%
3 643
1.9%
2 563
1.6%
1 579
1.7%

AcademicDegreeLevel
Categorical

High correlation  Missing 

Distinct7
Distinct (%)0.1%
Missing26835
Missing (%)78.0%
Memory size3.3 MiB
UB
3632 
GM
3106 
GP
569 
GD
 
173
GC
 
74
Other values (2)
 
28

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters15164
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUB
2nd rowGP
3rd rowGP
4th rowGM
5th rowGM

Common Values

ValueCountFrequency (%)
UB 3632
 
10.6%
GM 3106
 
9.0%
GP 569
 
1.7%
GD 173
 
0.5%
GC 74
 
0.2%
UC 19
 
0.1%
UG 9
 
< 0.1%
(Missing) 26835
78.0%

Length

2025-04-04T19:11:11.557604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T19:11:11.693280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ub 3632
47.9%
gm 3106
41.0%
gp 569
 
7.5%
gd 173
 
2.3%
gc 74
 
1.0%
uc 19
 
0.3%
ug 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G 3931
25.9%
U 3660
24.1%
B 3632
24.0%
M 3106
20.5%
P 569
 
3.8%
D 173
 
1.1%
C 93
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 3931
25.9%
U 3660
24.1%
B 3632
24.0%
M 3106
20.5%
P 569
 
3.8%
D 173
 
1.1%
C 93
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 3931
25.9%
U 3660
24.1%
B 3632
24.0%
M 3106
20.5%
P 569
 
3.8%
D 173
 
1.1%
C 93
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 3931
25.9%
U 3660
24.1%
B 3632
24.0%
M 3106
20.5%
P 569
 
3.8%
D 173
 
1.1%
C 93
 
0.6%

PreferredAddressType
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing4031
Missing (%)11.7%
Memory size3.3 MiB
HOME
28697 
BUSN
 
973
CAMP
 
645
OTR
 
71

Length

Max length4
Median length4
Mean length3.9976634
Min length3

Characters and Unicode

Total characters121473
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOME
2nd rowHOME
3rd rowHOME
4th rowHOME
5th rowHOME

Common Values

ValueCountFrequency (%)
HOME 28697
83.4%
BUSN 973
 
2.8%
CAMP 645
 
1.9%
OTR 71
 
0.2%
(Missing) 4031
 
11.7%

Length

2025-04-04T19:11:11.878331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T19:11:11.963414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home 28697
94.4%
busn 973
 
3.2%
camp 645
 
2.1%
otr 71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
M 29342
24.2%
O 28768
23.7%
H 28697
23.6%
E 28697
23.6%
B 973
 
0.8%
U 973
 
0.8%
S 973
 
0.8%
N 973
 
0.8%
C 645
 
0.5%
A 645
 
0.5%
Other values (3) 787
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 29342
24.2%
O 28768
23.7%
H 28697
23.6%
E 28697
23.6%
B 973
 
0.8%
U 973
 
0.8%
S 973
 
0.8%
N 973
 
0.8%
C 645
 
0.5%
A 645
 
0.5%
Other values (3) 787
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 29342
24.2%
O 28768
23.7%
H 28697
23.6%
E 28697
23.6%
B 973
 
0.8%
U 973
 
0.8%
S 973
 
0.8%
N 973
 
0.8%
C 645
 
0.5%
A 645
 
0.5%
Other values (3) 787
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 29342
24.2%
O 28768
23.7%
H 28697
23.6%
E 28697
23.6%
B 973
 
0.8%
U 973
 
0.8%
S 973
 
0.8%
N 973
 
0.8%
C 645
 
0.5%
A 645
 
0.5%
Other values (3) 787
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
False
23160 
True
11257 
ValueCountFrequency (%)
False 23160
67.3%
True 11257
32.7%
2025-04-04T19:11:12.020027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ConsecutiveDonorYears
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1383619
Minimum0
Maximum36
Zeros18180
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:12.099315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum36
Range36
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4247456
Coefficient of variation (CV)2.1300306
Kurtosis37.9043
Mean1.1383619
Median Absolute Deviation (MAD)0
Skewness5.1020811
Sum39179
Variance5.879391
MonotonicityNot monotonic
2025-04-04T19:11:12.217096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 18180
52.8%
1 9675
28.1%
2 2505
 
7.3%
3 1418
 
4.1%
4 689
 
2.0%
5 468
 
1.4%
6 344
 
1.0%
7 233
 
0.7%
8 155
 
0.5%
10 132
 
0.4%
Other values (23) 618
 
1.8%
ValueCountFrequency (%)
0 18180
52.8%
1 9675
28.1%
2 2505
 
7.3%
3 1418
 
4.1%
4 689
 
2.0%
5 468
 
1.4%
6 344
 
1.0%
7 233
 
0.7%
8 155
 
0.5%
9 123
 
0.4%
ValueCountFrequency (%)
36 1
 
< 0.1%
35 3
 
< 0.1%
34 5
< 0.1%
30 8
< 0.1%
28 8
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 6
< 0.1%
24 7
< 0.1%
23 4
< 0.1%

LastFiscalYearDonation
Real number (ℝ)

Zeros 

Distinct121
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0662754
Minimum0
Maximum850
Zeros32231
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:12.348320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum850
Range850
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39.745752
Coefficient of variation (CV)6.5519201
Kurtosis135.73426
Mean6.0662754
Median Absolute Deviation (MAD)0
Skewness10.359262
Sum208783
Variance1579.7248
MonotonicityNot monotonic
2025-04-04T19:11:12.482780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32231
93.6%
1 494
 
1.4%
100 192
 
0.6%
50 185
 
0.5%
25 149
 
0.4%
120 111
 
0.3%
200 75
 
0.2%
40 66
 
0.2%
20 66
 
0.2%
500 53
 
0.2%
Other values (111) 795
 
2.3%
ValueCountFrequency (%)
0 32231
93.6%
1 494
 
1.4%
2 27
 
0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
5 30
 
0.1%
6 7
 
< 0.1%
10 44
 
0.1%
11 4
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
850 2
 
< 0.1%
826 2
 
< 0.1%
750 7
< 0.1%
715 2
 
< 0.1%
700 4
< 0.1%
652 1
 
< 0.1%
600 5
< 0.1%
550 4
< 0.1%
510 4
< 0.1%
509 1
 
< 0.1%

Donation2FiscalYearsAgo
Real number (ℝ)

Zeros 

Distinct128
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4540779
Minimum0
Maximum950
Zeros32156
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:12.613628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum950
Range950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation41.763763
Coefficient of variation (CV)6.4709109
Kurtosis128.16301
Mean6.4540779
Median Absolute Deviation (MAD)0
Skewness10.084023
Sum222130
Variance1744.2119
MonotonicityNot monotonic
2025-04-04T19:11:12.746109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32156
93.4%
1 440
 
1.3%
50 185
 
0.5%
100 178
 
0.5%
25 151
 
0.4%
120 118
 
0.3%
200 85
 
0.2%
5 76
 
0.2%
20 72
 
0.2%
10 65
 
0.2%
Other values (118) 891
 
2.6%
ValueCountFrequency (%)
0 32156
93.4%
1 440
 
1.3%
2 28
 
0.1%
3 4
 
< 0.1%
5 76
 
0.2%
6 4
 
< 0.1%
8 2
 
< 0.1%
10 65
 
0.2%
12 1
 
< 0.1%
14 2
 
< 0.1%
ValueCountFrequency (%)
950 1
 
< 0.1%
935 1
 
< 0.1%
900 1
 
< 0.1%
804 1
 
< 0.1%
800 4
< 0.1%
750 1
 
< 0.1%
700 2
< 0.1%
690 1
 
< 0.1%
670 1
 
< 0.1%
664 3
< 0.1%

Donation3FiscalYearsAgo
Real number (ℝ)

Zeros 

Distinct133
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4317924
Minimum0
Maximum995
Zeros32060
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:12.899971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum995
Range995
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.092869
Coefficient of variation (CV)6.2335452
Kurtosis131.04004
Mean6.4317924
Median Absolute Deviation (MAD)0
Skewness10.083688
Sum221363
Variance1607.4381
MonotonicityNot monotonic
2025-04-04T19:11:13.047731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32060
93.2%
1 371
 
1.1%
100 224
 
0.7%
50 183
 
0.5%
25 166
 
0.5%
20 91
 
0.3%
200 88
 
0.3%
40 87
 
0.3%
5 86
 
0.2%
120 80
 
0.2%
Other values (123) 981
 
2.9%
ValueCountFrequency (%)
0 32060
93.2%
1 371
 
1.1%
2 6
 
< 0.1%
3 2
 
< 0.1%
5 86
 
0.2%
6 12
 
< 0.1%
7 3
 
< 0.1%
8 10
 
< 0.1%
10 58
 
0.2%
11 11
 
< 0.1%
ValueCountFrequency (%)
995 1
 
< 0.1%
802 1
 
< 0.1%
800 4
< 0.1%
750 2
 
< 0.1%
720 2
 
< 0.1%
700 2
 
< 0.1%
675 5
< 0.1%
600 7
< 0.1%
590 3
< 0.1%
560 3
< 0.1%

Donation4FiscalYearsAgo
Real number (ℝ)

Zeros 

Distinct130
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2285498
Minimum0
Maximum925
Zeros32407
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:13.186213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum925
Range925
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.052117
Coefficient of variation (CV)6.430408
Kurtosis143.05482
Mean6.2285498
Median Absolute Deviation (MAD)0
Skewness10.431776
Sum214368
Variance1604.1721
MonotonicityNot monotonic
2025-04-04T19:11:13.320024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32407
94.2%
100 215
 
0.6%
50 194
 
0.6%
25 172
 
0.5%
1 130
 
0.4%
40 97
 
0.3%
5 86
 
0.2%
20 86
 
0.2%
200 81
 
0.2%
150 58
 
0.2%
Other values (120) 891
 
2.6%
ValueCountFrequency (%)
0 32407
94.2%
1 130
 
0.4%
2 3
 
< 0.1%
4 1
 
< 0.1%
5 86
 
0.2%
6 3
 
< 0.1%
7 3
 
< 0.1%
10 56
 
0.2%
11 2
 
< 0.1%
15 43
 
0.1%
ValueCountFrequency (%)
925 2
 
< 0.1%
917 4
< 0.1%
880 1
 
< 0.1%
750 4
< 0.1%
700 3
< 0.1%
640 1
 
< 0.1%
613 1
 
< 0.1%
600 5
< 0.1%
550 2
 
< 0.1%
508 2
 
< 0.1%

Donation5FiscalYearsAgo
Real number (ℝ)

Zeros 

Distinct146
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0019177
Minimum0
Maximum992
Zeros32551
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:13.452852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum992
Range992
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.237324
Coefficient of variation (CV)6.704078
Kurtosis157.99652
Mean6.0019177
Median Absolute Deviation (MAD)0
Skewness11.080816
Sum206568
Variance1619.0422
MonotonicityNot monotonic
2025-04-04T19:11:13.603507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32551
94.6%
100 237
 
0.7%
25 184
 
0.5%
50 133
 
0.4%
1 127
 
0.4%
40 83
 
0.2%
200 79
 
0.2%
20 66
 
0.2%
10 61
 
0.2%
144 57
 
0.2%
Other values (136) 839
 
2.4%
ValueCountFrequency (%)
0 32551
94.6%
1 127
 
0.4%
2 9
 
< 0.1%
3 6
 
< 0.1%
5 27
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
10 61
 
0.2%
11 2
 
< 0.1%
ValueCountFrequency (%)
992 2
< 0.1%
901 2
< 0.1%
825 1
 
< 0.1%
802 1
 
< 0.1%
800 2
< 0.1%
750 4
< 0.1%
700 2
< 0.1%
660 4
< 0.1%
652 3
< 0.1%
640 1
 
< 0.1%

CurrentFiscalYearDonation
Real number (ℝ)

Zeros 

Distinct113
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6223669
Minimum0
Maximum988
Zeros32663
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:13.758159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum988
Range988
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.155082
Coefficient of variation (CV)7.1420246
Kurtosis164.23114
Mean5.6223669
Median Absolute Deviation (MAD)0
Skewness11.446759
Sum193505
Variance1612.4306
MonotonicityNot monotonic
2025-04-04T19:11:13.905266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32663
94.9%
1 303
 
0.9%
100 203
 
0.6%
50 164
 
0.5%
25 126
 
0.4%
120 89
 
0.3%
110 81
 
0.2%
500 46
 
0.1%
200 45
 
0.1%
60 37
 
0.1%
Other values (103) 660
 
1.9%
ValueCountFrequency (%)
0 32663
94.9%
1 303
 
0.9%
2 6
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 28
 
0.1%
10 33
 
0.1%
12 1
 
< 0.1%
15 13
 
< 0.1%
18 3
 
< 0.1%
ValueCountFrequency (%)
988 2
 
< 0.1%
800 10
< 0.1%
750 3
 
< 0.1%
725 1
 
< 0.1%
700 4
 
< 0.1%
660 2
 
< 0.1%
630 2
 
< 0.1%
610 1
 
< 0.1%
600 6
< 0.1%
550 4
 
< 0.1%

CumulativeDonationAmount
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1593
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23690.67
Minimum0
Maximum1.2221854 × 108
Zeros13043
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-04T19:11:14.046679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median250
Q31440
95-th percentile13090
Maximum1.2221854 × 108
Range1.2221854 × 108
Interquartile range (IQR)1440

Descriptive statistics

Standard deviation1139517.4
Coefficient of variation (CV)48.099838
Kurtosis9588.7414
Mean23690.67
Median Absolute Deviation (MAD)250
Skewness94.326728
Sum8.1536179 × 108
Variance1.2984999 × 1012
MonotonicityNot monotonic
2025-04-04T19:11:14.209030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13043
37.9%
1000 1254
 
3.6%
500 1226
 
3.6%
250 1210
 
3.5%
10 1096
 
3.2%
1200 645
 
1.9%
100 633
 
1.8%
200 568
 
1.7%
2000 487
 
1.4%
400 483
 
1.4%
Other values (1583) 13772
40.0%
ValueCountFrequency (%)
0 13043
37.9%
10 1096
 
3.2%
20 135
 
0.4%
30 97
 
0.3%
40 63
 
0.2%
50 463
 
1.3%
60 47
 
0.1%
70 32
 
0.1%
80 26
 
0.1%
90 11
 
< 0.1%
ValueCountFrequency (%)
122218540 2
< 0.1%
102005740 1
< 0.1%
34551880 1
< 0.1%
24404600 1
< 0.1%
23929200 1
< 0.1%
15942860 2
< 0.1%
15100000 1
< 0.1%
15000000 1
< 0.1%
14691600 1
< 0.1%
12600000 1
< 0.1%

DonorIndicatorFlag.
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
21374 
False
13043 
ValueCountFrequency (%)
True 21374
62.1%
False 13043
37.9%
2025-04-04T19:11:14.296959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

STATE
Text

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2025-04-04T19:11:14.474542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0703141
Min length2

Characters and Unicode

Total characters71254
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowVA
2nd rowTX
3rd rowIN
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 7570
22.0%
tx 1731
 
5.0%
pa 1426
 
4.1%
ny 1412
 
4.1%
none 1210
 
3.5%
il 1031
 
3.0%
fl 1002
 
2.9%
oh 909
 
2.6%
mi 783
 
2.3%
va 778
 
2.3%
Other values (51) 16565
48.1%
2025-04-04T19:11:14.768615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 14427
20.2%
C 9458
13.3%
N 6900
 
9.7%
M 4220
 
5.9%
I 4132
 
5.8%
T 3191
 
4.5%
L 2991
 
4.2%
O 2858
 
4.0%
Y 2141
 
3.0%
K 1815
 
2.5%
Other values (17) 19121
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14427
20.2%
C 9458
13.3%
N 6900
 
9.7%
M 4220
 
5.9%
I 4132
 
5.8%
T 3191
 
4.5%
L 2991
 
4.2%
O 2858
 
4.0%
Y 2141
 
3.0%
K 1815
 
2.5%
Other values (17) 19121
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14427
20.2%
C 9458
13.3%
N 6900
 
9.7%
M 4220
 
5.9%
I 4132
 
5.8%
T 3191
 
4.5%
L 2991
 
4.2%
O 2858
 
4.0%
Y 2141
 
3.0%
K 1815
 
2.5%
Other values (17) 19121
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14427
20.2%
C 9458
13.3%
N 6900
 
9.7%
M 4220
 
5.9%
I 4132
 
5.8%
T 3191
 
4.5%
L 2991
 
4.2%
O 2858
 
4.0%
Y 2141
 
3.0%
K 1815
 
2.5%
Other values (17) 19121
26.8%

CITY
Text

Distinct10830
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2025-04-04T19:11:15.106862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length24
Mean length7.9456373
Min length3

Characters and Unicode

Total characters273465
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5941 ?
Unique (%)17.3%

Sample

1st rowWilliamsburg
2nd rowPort Arthur
3rd rowMarysville
4th rowMoreno Valley
5th rowSan Jose
ValueCountFrequency (%)
malibu 5849
 
14.1%
none 1210
 
2.9%
city 535
 
1.3%
new 368
 
0.9%
san 291
 
0.7%
saint 274
 
0.7%
lake 273
 
0.7%
fort 228
 
0.6%
apo 222
 
0.5%
springs 222
 
0.5%
Other values (9123) 31970
77.1%
2025-04-04T19:11:15.564871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25853
 
9.5%
e 22705
 
8.3%
l 22120
 
8.1%
i 20030
 
7.3%
o 19040
 
7.0%
n 18604
 
6.8%
r 14939
 
5.5%
t 12267
 
4.5%
u 11147
 
4.1%
s 9722
 
3.6%
Other values (44) 97038
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 25853
 
9.5%
e 22705
 
8.3%
l 22120
 
8.1%
i 20030
 
7.3%
o 19040
 
7.0%
n 18604
 
6.8%
r 14939
 
5.5%
t 12267
 
4.5%
u 11147
 
4.1%
s 9722
 
3.6%
Other values (44) 97038
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 25853
 
9.5%
e 22705
 
8.3%
l 22120
 
8.1%
i 20030
 
7.3%
o 19040
 
7.0%
n 18604
 
6.8%
r 14939
 
5.5%
t 12267
 
4.5%
u 11147
 
4.1%
s 9722
 
3.6%
Other values (44) 97038
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 25853
 
9.5%
e 22705
 
8.3%
l 22120
 
8.1%
i 20030
 
7.3%
o 19040
 
7.0%
n 18604
 
6.8%
r 14939
 
5.5%
t 12267
 
4.5%
u 11147
 
4.1%
s 9722
 
3.6%
Other values (44) 97038
35.5%

Interactions

2025-04-04T19:10:10.305155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:10.491248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:17.734724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:20.562744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.190838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:34.574303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:41.431119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.407655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.164063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.419200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:16.290846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:10.604235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:17.914370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:20.675846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.289781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:34.682921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:41.590393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.508049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.316130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.520731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:17.821108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:10.711168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.067496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:20.794649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.411153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:34.798541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:41.756723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.625909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.507727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.640649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:23.752963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:10.822045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.254484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:20.927890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.528929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:34.921604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:41.924188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.744064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.636205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.752939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:28.763603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:10.936466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.423756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:21.047915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.643933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.032532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.081501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.849858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.740373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.856841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:35.841248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:11.039335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.601185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:21.170089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.748551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.142874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.230774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:49.972881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.844239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:03.960839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:41.163337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:11.149577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.716377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:21.293852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.852562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.263749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.379695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:50.078696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:56.960975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:04.073005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:47.143719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:11.490488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.834882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:21.413125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:27.962907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.366188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.536150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:50.186690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:57.067793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:04.181305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:52.162470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:11.612673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:18.944208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:21.882805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:28.500057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.482827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.692916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:50.290527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:57.184442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:04.285087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:59.307153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:11.740128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:19.065407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:22.033830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:28.627109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:35.603578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:42.878791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:50.412481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:09:57.311587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T19:10:04.429213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-04T19:11:15.690176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AcademicDegreeLevelConsecutiveDonorYearsCumulativeDonationAmountCurrentFiscalYearDonationDonation2FiscalYearsAgoDonation3FiscalYearsAgoDonation4FiscalYearsAgoDonation5FiscalYearsAgoDonorAgeDonorIndicatorFlag.GenderIdentityHasEmailFlagHasInvolvementFlagIsAlumnusFlagIsParentFlagLastFiscalYearDonationMaritalStatusPreferredAddressTypeWealthRating
AcademicDegreeLevel1.0000.0490.0000.0000.0000.0000.0000.0040.0880.0210.0380.0490.1521.0000.0170.0000.0000.0440.140
ConsecutiveDonorYears0.0491.0000.151-0.005-0.0050.1690.0100.2560.0340.1420.0250.0370.1640.0690.0370.0030.0680.0180.043
CumulativeDonationAmount0.0000.1511.0000.1730.2020.2210.1980.2090.0200.9770.0000.0410.1110.0550.0000.1830.1490.0000.003
CurrentFiscalYearDonation0.000-0.0050.1731.000-0.007-0.0090.000-0.0060.0040.1190.0210.0120.0040.0090.0160.0030.0000.000-0.007
Donation2FiscalYearsAgo0.000-0.0050.202-0.0071.0000.2490.041-0.005-0.0030.1230.0000.0000.0050.0000.0030.0010.0000.000-0.002
Donation3FiscalYearsAgo0.0000.1690.221-0.0090.2491.0000.0750.0190.0040.1250.0000.0020.0000.0000.0280.0140.0000.0090.027
Donation4FiscalYearsAgo0.0000.0100.1980.0000.0410.0751.0000.0070.0100.1220.0000.0000.0070.0000.0120.0070.0000.032-0.036
Donation5FiscalYearsAgo0.0040.2560.209-0.006-0.0050.0190.0071.0000.0270.1210.0000.0110.0680.0260.020-0.0050.0160.0160.011
DonorAge0.0880.0340.0200.004-0.0030.0040.0100.0271.0000.0250.0660.2680.1420.2830.113-0.0050.0500.141-0.004
DonorIndicatorFlag.0.0210.1420.9770.1190.1230.1250.1220.1210.0251.0000.0220.0010.0370.0150.0080.1260.0380.0090.000
GenderIdentity0.0380.0250.0000.0210.0000.0000.0000.0000.0660.0221.0000.0590.1560.0820.0460.0000.2070.0220.000
HasEmailFlag0.0490.0370.0410.0120.0000.0020.0000.0110.2680.0010.0591.0000.3070.3640.1640.0100.2130.1880.064
HasInvolvementFlag0.1520.1640.1110.0040.0050.0000.0070.0680.1420.0370.1560.3071.0000.4370.0310.0000.0860.0590.027
IsAlumnusFlag1.0000.0690.0550.0090.0000.0000.0000.0260.2830.0150.0820.3640.4371.0000.0870.0040.1370.0470.000
IsParentFlag0.0170.0370.0000.0160.0030.0280.0120.0200.1130.0080.0460.1640.0310.0871.0000.0000.3810.0470.020
LastFiscalYearDonation0.0000.0030.1830.0030.0010.0140.007-0.005-0.0050.1260.0000.0100.0000.0040.0001.0000.0000.0010.021
MaritalStatus0.0000.0680.1490.0000.0000.0000.0000.0160.0500.0380.2070.2130.0860.1370.3810.0001.0000.0490.000
PreferredAddressType0.0440.0180.0000.0000.0000.0090.0320.0160.1410.0090.0220.1880.0590.0470.0470.0010.0491.0000.037
WealthRating0.1400.0430.003-0.007-0.0020.027-0.0360.011-0.0040.0000.0000.0640.0270.0000.0200.0210.0000.0371.000

Missing values

2025-04-04T19:11:08.920804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T19:11:09.214241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-04T19:11:09.696888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DonorAgeMaritalStatusGenderIdentityIsMemberFlagIsAlumnusFlagIsParentFlagHasInvolvementFlagWealthRatingAcademicDegreeLevelPreferredAddressTypeHasEmailFlagConsecutiveDonorYearsLastFiscalYearDonationDonation2FiscalYearsAgoDonation3FiscalYearsAgoDonation4FiscalYearsAgoDonation5FiscalYearsAgoCurrentFiscalYearDonationCumulativeDonationAmountDonorIndicatorFlag.STATECITY
042MarriedFemaleNNNNNaNNaNHOMEN1000000100YVAWilliamsburg
133NaNFemaleNYNYNaNUBNaNY000000021000YTXPort Arthur
331NaNFemaleNYNYNaNNaNHOMEY000000000NINMarysville
468NaNFemaleNNNNNaNNaNHOMEY00000005050YCAMoreno Valley
557NaNMaleNNNNNaNNaNHOMEN000000000NCASan Jose
642NaNMaleNNNNNaNNaNHOMEY300000000NCAMalibu
742MarriedFemaleNNNNNaNNaNHOMEN10000001700YKSColby
842SingleUknownNNNYNaNNaNHOMEN050000050YCARackerby
942MarriedFemaleNNYNNaNNaNHOMEN0000000150YMIShingleton
1040NaNMaleNNNNNaNNaNHOMEY000000000NWABellevue
DonorAgeMaritalStatusGenderIdentityIsMemberFlagIsAlumnusFlagIsParentFlagHasInvolvementFlagWealthRatingAcademicDegreeLevelPreferredAddressTypeHasEmailFlagConsecutiveDonorYearsLastFiscalYearDonationDonation2FiscalYearsAgoDonation3FiscalYearsAgoDonation4FiscalYearsAgoDonation5FiscalYearsAgoCurrentFiscalYearDonationCumulativeDonationAmountDonorIndicatorFlag.STATECITY
3449842NaNMaleNNNNNaNNaNHOMEN12000000200YCAMalibu
3449957NaNFemaleNNNNNaNNaNHOMEN0000000250YCAMalibu
3450042NaNFemaleNNNYNaNNaNHOMEY30100002900YDCWashington
3450142NaNFemaleNNNYNaNNaNBUSNN30000002650YKYSmilax
3450242MarriedMaleNNYN2.0NaNHOMEY000000000NIARidgeway
3450342NaNFemaleNNNNNaNNaNHOMEN000000000NNJLafayette
3450424NaNMaleNNNN5.0NaNCAMPY0000000800YNCCharlotte
3450527NaNFemaleNYNYNaNUBHOMEY000000000NKYLiberty
3450646MarriedFemaleNNNYNaNNaNHOMEY1000120001200YFLGoldenrod
3450758MarriedFemaleNYNYNaNUBHOMEY100000000NNYNew York

Duplicate rows

Most frequently occurring

DonorAgeMaritalStatusGenderIdentityIsMemberFlagIsAlumnusFlagIsParentFlagHasInvolvementFlagWealthRatingAcademicDegreeLevelPreferredAddressTypeHasEmailFlagConsecutiveDonorYearsLastFiscalYearDonationDonation2FiscalYearsAgoDonation3FiscalYearsAgoDonation4FiscalYearsAgoDonation5FiscalYearsAgoCurrentFiscalYearDonationCumulativeDonationAmountDonorIndicatorFlag.STATECITY# duplicates
37042NaNMaleNNNNNaNNaNHOMEN100000000NCAMalibu114
26142NaNFemaleNNNNNaNNaNHOMEN100000000NCAMalibu90
14442MarriedMaleNNNNNaNNaNHOMEN000000000NCAMalibu33
15542MarriedMaleNNNNNaNNaNHOMEN100000000NCAMalibu30
18742MarriedMaleNNYNNaNNaNHOMEY000000000NCAMalibu30
22042SingleMaleNNNNNaNNaNNaNN000000000NCAMalibu29
10242MarriedFemaleNNNNNaNNaNHOMEN000000000NCAMalibu28
23342SingleUknownNNNYNaNNaNHOMEN000000000NCAMalibu28
41242NaNMaleNNNNNaNNaNHOMEN200000000NCAMalibu24
25142NaNFemaleNNNNNaNNaNHOMEN000000000NCAMalibu23